The impact of first wave of the SARS-CoV-2 2019 pandemic in Poland on characteristics and outcomes of patients hospitalized due to stable coronary artery disease
Bibliographic record
Abstract
BACKGROUND: An investigation of baseline characteristics, treatment, and outcomes in patients with stable coronary disease after the first wave of the severe acute respiratory syndrome coronavirus 2 (SARS- -CoV-2) pandemic may provide valuable data and is beneficial for public health strategy in upcoming years. METHODS: A multi-institutional registry, including 10 cardiology departments, was searched for patients admitted from June 2020 to October 2020. The baseline characteristics (age, gender, symptoms, comorbidities), treatment (non-invasive, invasive, surgical), and hospitalization outcome (mortality, myocardial infarction, stroke, composite endpoint - major adverse cardiac and cerebrovascular events [MACCE]) were evaluated. The comparison was made to parameters presented by patients from the same timeframe in 2019 (June-October). Multivariable analysis was performed. RESULTS: Number of hospitalized stable patients following lockdown was lower (2498 vs. 1903; p < 0.0001). They were younger (68.0 vs. 69.0; p < 0.019), more likely to present with hypertension (88.5% vs. 77.5%; p < 0.0001), diabetes (35.7% vs. 31.5%; p = 0.003), hyperlipidemia (67.9% vs. 55.4%; p < 0.0001), obesity (35.8% vs. 31.3%; p = 0.002), and more pronounced symptoms (Canadian Cardiovascular Society [CCS] III and CCS class IV angina: 30.4% vs. 26.5%; p = 0.005). They underwent percutaneous treatment more often (35.0% vs. 25.9%; p < 0.0001) and were less likely to be referred for surgery (3.7% vs. 4.9%; p = 0.0001). There were no significant differences in hospitalization outcome. New York Heart Association (NYHA) class IV for heart failure was a risk factor for both mortality and MACCE in multivariate analysis. CONCLUSIONS: The SARS-CoV-2 2019 pandemic affected the characteristics and hospitalization course of stable angina patients hospitalized following the first wave. The hospitalization outcome was similar in the analyzed time intervals. The higher prevalence of comorbidities raises concern regarding upcoming years.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".